Complex projects such as the planning and production of large commercial or military aircraft require the scheduling and coordination of a plurality of resources. The resources to be coordinated may include materials, component parts, personnel, machinery and factory floor space, in addition to other resources. Integration and coordination is particularly important in complex projects since higher-order effects and interactions can adversely affect the cost of the project, the time required for completion of the project, and the risk of failure to deliver the required content. In addition, other variables of importance such as the overall efficiency of the project need to be modeled and measured.
In a number of known methods, the planning process generally includes the processing of input data that defines task dependencies and estimated task durations. Task dependencies generally express relationships between various tasks, so that the various tasks may be properly ordered. For example, in the construction of large commercial aircraft, a material such as an aluminum sheet material must be procured before fuselage panels may be fabricated. The input data may be processed in accordance with a number of different techniques to arrange the various tasks into an ordered set. In some cases, a multiplicity of different paths may result from processing the input data, which may include multiple paths that could end up being critical. The critical path may be the sequence of tasks throughout the project that determines its duration, and may be the path with the least amount of scheduling flexibility (float). Accordingly, it is the path along which no delay in the provision of a necessary resource may occur without delaying the entire project, and is thus of central importance in project execution. The manufacturing process may therefore be analyzed based upon relationships between the various individual tasks comprising the process, and upon a critical path for the process. The critical path may shift from a first task set to another task set as resource delays occur and/or task durations vary from their estimated values. Accordingly, the critical path is not fixed, and may change.
Although existing process planning methods are useful, they nevertheless exhibit several drawbacks, and thus may not accurately represent a selected process. Existing methods of process planning rely on discrete schedule estimates and task precedence relationships rather than the data-driven relationships in the network of deliverables. As such, they devolve into a schedule-based (as opposed to process-based) planning approach. Plans or models based on these existing methods are less robust, in adapting to variation in the durations of planned activities, and how the interactions between activities are modeled. For example, the different estimating methods utilized for scheduling purposes may be one such source of variation. As activities are executed, compounding sources of variation may contribute to deviations in the planned durations. Further, management policies that are designed to focus on schedule performance only may also drive changes in how the interactions of activities are modeled. As a result of these multiple and compounding sources of uncertainty and variation, traditional planning methods are not suitable for application in the automatic generation of computer models for complex process planning and management. And without a capability to recognize and analyze data-driven constraints, the current methods are not capable of producing a robust, predictive model that will support risk assessment and inform decision making that will result in the best possible outcome.